kvfrans / clipdraw

Synthesize drawings to match a text prompt

Cold

Public
5.6K runs
T4
GitHub
Paper

Iterate in playground

Run with an API

Playground API Examples README Versions

Prediction

kvfrans/clipdraw:5793aa325d9e66f63b48a248291b7d552e03712bbf481224215d31aa906c609b

Model

kvfrans/clipdraw:5793aa32

43runp6r7zax5mr2bntgbe6kia

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

almost 4 years ago

Input

prompt: self
num_paths: 300
num_iterations: 1000
display_frequency: "10"

{
  "prompt": "self",
  "num_paths": 300,
  "num_iterations": 1000,
  "display_frequency": "10"
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run kvfrans/clipdraw using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "kvfrans/clipdraw:5793aa325d9e66f63b48a248291b7d552e03712bbf481224215d31aa906c609b",
  {
    input: {
      prompt: "self",
      num_paths: 300,
      num_iterations: 1000,
      display_frequency: "10"
    }
  }
);

console.log(output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

Run kvfrans/clipdraw using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "kvfrans/clipdraw:5793aa325d9e66f63b48a248291b7d552e03712bbf481224215d31aa906c609b",
    input={
        "prompt": "self",
        "num_paths": 300,
        "num_iterations": 1000,
        "display_frequency": "10"
    }
)

# The kvfrans/clipdraw model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
    # https://replicate.com/kvfrans/clipdraw/api#output-schema
    print(item)

To learn more, take a look at the guide on getting started with Python.

Run kvfrans/clipdraw using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "kvfrans/clipdraw:5793aa325d9e66f63b48a248291b7d552e03712bbf481224215d31aa906c609b",
    "input": {
      "prompt": "self",
      "num_paths": 300,
      "num_iterations": 1000,
      "display_frequency": "10"
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2021-10-01T01:07:01.396246Z",
  "created_at": "2021-10-01T00:40:30.693784Z",
  "data_removed": false,
  "error": null,
  "id": "43runp6r7zax5mr2bntgbe6kia",
  "input": {
    "prompt": "self",
    "num_paths": 300,
    "num_iterations": 1000,
    "display_frequency": "10"
  },
  "logs": "iteration: 0, render:loss: -0.7646484375\niteration: 10, render:loss: -1.0439453125\niteration: 20, render:loss: -1.166015625\niteration: 30, render:loss: -1.353515625\niteration: 40, render:loss: -1.37109375\niteration: 50, render:loss: -1.380859375\niteration: 60, render:loss: -1.392578125\niteration: 70, render:loss: -1.400390625\niteration: 80, render:loss: -1.4296875\niteration: 90, render:loss: -1.4404296875\niteration: 100, render:loss: -1.427734375\niteration: 110, render:loss: -1.423828125\niteration: 120, render:loss: -1.45703125\niteration: 130, render:loss: -1.46484375\niteration: 140, render:loss: -1.431640625\niteration: 150, render:loss: -1.4267578125\niteration: 160, render:loss: -1.376953125\niteration: 170, render:loss: -1.462890625\niteration: 180, render:loss: -1.474609375\niteration: 190, render:loss: -1.490234375\niteration: 200, render:loss: -1.37890625\niteration: 210, render:loss: -1.462890625\niteration: 220, render:loss: -1.4609375\niteration: 230, render:loss: -1.4873046875\niteration: 240, render:loss: -1.486328125\niteration: 250, render:loss: -1.4814453125\niteration: 260, render:loss: -1.4833984375\niteration: 270, render:loss: -1.4775390625\niteration: 280, render:loss: -1.439453125\niteration: 290, render:loss: -1.435546875\niteration: 300, render:loss: -1.4892578125\niteration: 310, render:loss: -1.46484375\niteration: 320, render:loss: -1.48828125\niteration: 330, render:loss: -1.4873046875\niteration: 340, render:loss: -1.501953125\niteration: 350, render:loss: -1.453125\niteration: 360, render:loss: -1.4892578125\niteration: 370, render:loss: -1.412109375\niteration: 380, render:loss: -1.4990234375\niteration: 390, render:loss: -1.46484375\niteration: 400, render:loss: -1.4892578125\niteration: 410, render:loss: -1.49609375\niteration: 420, render:loss: -1.4921875\niteration: 430, render:loss: -1.5107421875\niteration: 440, render:loss: -1.5\niteration: 450, render:loss: -1.4033203125\niteration: 460, render:loss: -1.484375\niteration: 470, render:loss: -1.4755859375\niteration: 480, render:loss: -1.498046875\niteration: 490, render:loss: -1.4326171875\niteration: 500, render:loss: -1.4736328125\niteration: 510, render:loss: -1.4912109375\niteration: 520, render:loss: -1.50390625\niteration: 530, render:loss: -1.5234375\niteration: 540, render:loss: -1.498046875\niteration: 550, render:loss: -1.5166015625\niteration: 560, render:loss: -1.52734375\niteration: 570, render:loss: -1.5380859375\niteration: 580, render:loss: -1.4599609375\niteration: 590, render:loss: -1.525390625\niteration: 600, render:loss: -1.548828125\niteration: 610, render:loss: -1.521484375\niteration: 620, render:loss: -1.525390625\niteration: 630, render:loss: -1.5224609375\niteration: 640, render:loss: -1.529296875\niteration: 650, render:loss: -1.5341796875\niteration: 660, render:loss: -1.5126953125\niteration: 670, render:loss: -1.546875\niteration: 680, render:loss: -1.541015625\niteration: 690, render:loss: -1.5361328125\niteration: 700, render:loss: -1.552734375\niteration: 710, render:loss: -1.55078125\niteration: 720, render:loss: -1.5546875\niteration: 730, render:loss: -1.5498046875\niteration: 740, render:loss: -1.544921875\niteration: 750, render:loss: -1.546875\niteration: 760, render:loss: -1.55078125\niteration: 770, render:loss: -1.548828125\niteration: 780, render:loss: -1.4814453125\niteration: 790, render:loss: -1.5556640625\niteration: 800, render:loss: -1.5556640625\niteration: 810, render:loss: -1.5712890625\niteration: 820, render:loss: -1.556640625\niteration: 830, render:loss: -1.5654296875\niteration: 840, render:loss: -1.578125\niteration: 850, render:loss: -1.5712890625\niteration: 860, render:loss: -1.568359375\niteration: 870, render:loss: -1.5712890625\niteration: 880, render:loss: -1.5693359375\niteration: 890, render:loss: -1.513671875\niteration: 900, render:loss: -1.58203125\niteration: 910, render:loss: -1.587890625\niteration: 920, render:loss: -1.5673828125\niteration: 930, render:loss: -1.5625\niteration: 940, render:loss: -1.56640625\niteration: 950, render:loss: -1.580078125\niteration: 960, render:loss: -1.55859375\niteration: 970, render:loss: -1.572265625\niteration: 980, render:loss: -1.572265625\niteration: 990, render:loss: -1.5615234375\niteration: 999, render:loss: -1.57421875",
  "metrics": {},
  "output": [
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    {
      "file": "https://replicate.delivery/mgxm/f334bf5d-42bb-4cb9-b7c9-85e1f6aac413/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/660d462a-c26a-453b-bc79-1433ae219a88/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/d8ea9aa9-3a27-4d57-aa43-0cc75c5ba0d3/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/6f41ab36-ac25-49ae-ab9d-a02b2a58e182/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/66027fde-d1ab-45ae-8c87-eee18dfbddd9/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/3d25332a-95c1-48f1-8478-19d5384f9191/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/7b4b2b2c-ca0d-43f4-8e5e-520d8e25e623/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/50df33ca-2100-49fa-833f-6765af24eddc/out.png"
    }
  ],
  "started_at": null,
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/43runp6r7zax5mr2bntgbe6kia",
    "cancel": "https://api.replicate.com/v1/predictions/43runp6r7zax5mr2bntgbe6kia/cancel"
  },
  "version": "5793aa325d9e66f63b48a248291b7d552e03712bbf481224215d31aa906c609b"
}

Tweak it ShareReport

iteration: 0, render:loss: -0.7646484375
iteration: 10, render:loss: -1.0439453125
iteration: 20, render:loss: -1.166015625
iteration: 30, render:loss: -1.353515625
iteration: 40, render:loss: -1.37109375
iteration: 50, render:loss: -1.380859375
iteration: 60, render:loss: -1.392578125
iteration: 70, render:loss: -1.400390625
iteration: 80, render:loss: -1.4296875
iteration: 90, render:loss: -1.4404296875
iteration: 100, render:loss: -1.427734375
iteration: 110, render:loss: -1.423828125
iteration: 120, render:loss: -1.45703125
iteration: 130, render:loss: -1.46484375
iteration: 140, render:loss: -1.431640625
iteration: 150, render:loss: -1.4267578125
iteration: 160, render:loss: -1.376953125
iteration: 170, render:loss: -1.462890625
iteration: 180, render:loss: -1.474609375
iteration: 190, render:loss: -1.490234375
iteration: 200, render:loss: -1.37890625
iteration: 210, render:loss: -1.462890625
iteration: 220, render:loss: -1.4609375
iteration: 230, render:loss: -1.4873046875
iteration: 240, render:loss: -1.486328125
iteration: 250, render:loss: -1.4814453125
iteration: 260, render:loss: -1.4833984375
iteration: 270, render:loss: -1.4775390625
iteration: 280, render:loss: -1.439453125
iteration: 290, render:loss: -1.435546875
iteration: 300, render:loss: -1.4892578125
iteration: 310, render:loss: -1.46484375
iteration: 320, render:loss: -1.48828125
iteration: 330, render:loss: -1.4873046875
iteration: 340, render:loss: -1.501953125
iteration: 350, render:loss: -1.453125
iteration: 360, render:loss: -1.4892578125
iteration: 370, render:loss: -1.412109375
iteration: 380, render:loss: -1.4990234375
iteration: 390, render:loss: -1.46484375
iteration: 400, render:loss: -1.4892578125
iteration: 410, render:loss: -1.49609375
iteration: 420, render:loss: -1.4921875
iteration: 430, render:loss: -1.5107421875
iteration: 440, render:loss: -1.5
iteration: 450, render:loss: -1.4033203125
iteration: 460, render:loss: -1.484375
iteration: 470, render:loss: -1.4755859375
iteration: 480, render:loss: -1.498046875
iteration: 490, render:loss: -1.4326171875
iteration: 500, render:loss: -1.4736328125
iteration: 510, render:loss: -1.4912109375
iteration: 520, render:loss: -1.50390625
iteration: 530, render:loss: -1.5234375
iteration: 540, render:loss: -1.498046875
iteration: 550, render:loss: -1.5166015625
iteration: 560, render:loss: -1.52734375
iteration: 570, render:loss: -1.5380859375
iteration: 580, render:loss: -1.4599609375
iteration: 590, render:loss: -1.525390625
iteration: 600, render:loss: -1.548828125
iteration: 610, render:loss: -1.521484375
iteration: 620, render:loss: -1.525390625
iteration: 630, render:loss: -1.5224609375
iteration: 640, render:loss: -1.529296875
iteration: 650, render:loss: -1.5341796875
iteration: 660, render:loss: -1.5126953125
iteration: 670, render:loss: -1.546875
iteration: 680, render:loss: -1.541015625
iteration: 690, render:loss: -1.5361328125
iteration: 700, render:loss: -1.552734375
iteration: 710, render:loss: -1.55078125
iteration: 720, render:loss: -1.5546875
iteration: 730, render:loss: -1.5498046875
iteration: 740, render:loss: -1.544921875
iteration: 750, render:loss: -1.546875
iteration: 760, render:loss: -1.55078125
iteration: 770, render:loss: -1.548828125
iteration: 780, render:loss: -1.4814453125
iteration: 790, render:loss: -1.5556640625
iteration: 800, render:loss: -1.5556640625
iteration: 810, render:loss: -1.5712890625
iteration: 820, render:loss: -1.556640625
iteration: 830, render:loss: -1.5654296875
iteration: 840, render:loss: -1.578125
iteration: 850, render:loss: -1.5712890625
iteration: 860, render:loss: -1.568359375
iteration: 870, render:loss: -1.5712890625
iteration: 880, render:loss: -1.5693359375
iteration: 890, render:loss: -1.513671875
iteration: 900, render:loss: -1.58203125
iteration: 910, render:loss: -1.587890625
iteration: 920, render:loss: -1.5673828125
iteration: 930, render:loss: -1.5625
iteration: 940, render:loss: -1.56640625
iteration: 950, render:loss: -1.580078125
iteration: 960, render:loss: -1.55859375
iteration: 970, render:loss: -1.572265625
iteration: 980, render:loss: -1.572265625
iteration: 990, render:loss: -1.5615234375
iteration: 999, render:loss: -1.57421875

Prediction

kvfrans/clipdraw:5793aa325d9e66f63b48a248291b7d552e03712bbf481224215d31aa906c609b

Model

kvfrans/clipdraw:5793aa32

usdvqwoudja73m6vnlcgjy5mwq

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

almost 4 years ago by @chenxwh

Input

prompt: Watercolor painting of an underwater submarine
num_paths: 300
num_iterations: 600
display_frequency: "10"

{
  "prompt": "Watercolor painting of an underwater submarine",
  "num_paths": 300,
  "num_iterations": 600,
  "display_frequency": "10"
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run kvfrans/clipdraw using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "kvfrans/clipdraw:5793aa325d9e66f63b48a248291b7d552e03712bbf481224215d31aa906c609b",
  {
    input: {
      prompt: "Watercolor painting of an underwater submarine",
      num_paths: 300,
      num_iterations: 600,
      display_frequency: "10"
    }
  }
);

console.log(output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

Run kvfrans/clipdraw using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "kvfrans/clipdraw:5793aa325d9e66f63b48a248291b7d552e03712bbf481224215d31aa906c609b",
    input={
        "prompt": "Watercolor painting of an underwater submarine",
        "num_paths": 300,
        "num_iterations": 600,
        "display_frequency": "10"
    }
)

# The kvfrans/clipdraw model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
    # https://replicate.com/kvfrans/clipdraw/api#output-schema
    print(item)

To learn more, take a look at the guide on getting started with Python.

Run kvfrans/clipdraw using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "kvfrans/clipdraw:5793aa325d9e66f63b48a248291b7d552e03712bbf481224215d31aa906c609b",
    "input": {
      "prompt": "Watercolor painting of an underwater submarine",
      "num_paths": 300,
      "num_iterations": 600,
      "display_frequency": "10"
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2021-10-01T01:11:26.264537Z",
  "created_at": "2021-10-01T01:01:13.493566Z",
  "data_removed": false,
  "error": null,
  "id": "usdvqwoudja73m6vnlcgjy5mwq",
  "input": {
    "prompt": "Watercolor painting of an underwater submarine",
    "num_paths": 300,
    "num_iterations": 600,
    "display_frequency": "10"
  },
  "logs": "iteration: 0, render:loss: -0.67529296875\niteration: 10, render:loss: -1.03125\niteration: 20, render:loss: -1.1787109375\niteration: 30, render:loss: -1.365234375\niteration: 40, render:loss: -1.529296875\niteration: 50, render:loss: -1.5712890625\niteration: 60, render:loss: -1.619140625\niteration: 70, render:loss: -1.662109375\niteration: 80, render:loss: -1.650390625\niteration: 90, render:loss: -1.7021484375\niteration: 100, render:loss: -1.7392578125\niteration: 110, render:loss: -1.7568359375\niteration: 120, render:loss: -1.794921875\niteration: 130, render:loss: -1.7451171875\niteration: 140, render:loss: -1.771484375\niteration: 150, render:loss: -1.7890625\niteration: 160, render:loss: -1.8291015625\niteration: 170, render:loss: -1.82421875\niteration: 180, render:loss: -1.798828125\niteration: 190, render:loss: -1.7822265625\niteration: 200, render:loss: -1.8056640625\niteration: 210, render:loss: -1.8291015625\niteration: 220, render:loss: -1.8095703125\niteration: 230, render:loss: -1.873046875\niteration: 240, render:loss: -1.814453125\niteration: 250, render:loss: -1.8828125\niteration: 260, render:loss: -1.814453125\niteration: 270, render:loss: -1.798828125\niteration: 280, render:loss: -1.8583984375\niteration: 290, render:loss: -1.9130859375\niteration: 300, render:loss: -1.845703125\niteration: 310, render:loss: -1.8701171875\niteration: 320, render:loss: -1.9306640625\niteration: 330, render:loss: -1.9609375\niteration: 340, render:loss: -1.8740234375\niteration: 350, render:loss: -1.7841796875\niteration: 360, render:loss: -1.9423828125\niteration: 370, render:loss: -1.8515625\niteration: 380, render:loss: -2.001953125\niteration: 390, render:loss: -2.001953125\niteration: 400, render:loss: -1.9482421875\niteration: 410, render:loss: -1.984375\niteration: 420, render:loss: -1.94921875\niteration: 430, render:loss: -2.037109375\niteration: 440, render:loss: -1.986328125\niteration: 450, render:loss: -1.9921875\niteration: 460, render:loss: -2.015625\niteration: 470, render:loss: -2.05078125\niteration: 480, render:loss: -2.078125\niteration: 490, render:loss: -2.041015625\niteration: 500, render:loss: -2.048828125\niteration: 510, render:loss: -2.09765625\niteration: 520, render:loss: -2.078125\niteration: 530, render:loss: -2.048828125\niteration: 540, render:loss: -2.083984375\niteration: 550, render:loss: -2.033203125\niteration: 560, render:loss: -2.140625\niteration: 570, render:loss: -2.08203125\niteration: 580, render:loss: -2.0234375\niteration: 590, render:loss: -2.126953125\niteration: 599, render:loss: -2.109375",
  "metrics": {
    "total_time": 612.770971
  },
  "output": [
    {
      "file": "https://replicate.delivery/mgxm/5a259bd7-48c6-49f9-91ad-dde184a43450/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/dbd2ac08-1845-43a3-b138-9c529404c4ab/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/aade56d7-09e3-495e-9605-1e161ff42aeb/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/af305f1f-901e-43df-bad2-1a1943c41fc2/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/e4460b5b-4a85-4f79-a515-dada2cf4455c/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/555c4422-b871-42b5-bba7-1f42495c89b8/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/ecc29dfe-f51c-455c-8594-26413e0566d7/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/2e7bdd12-e1ad-44ad-bdb6-febce6644460/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/4dcaacd1-1ba1-4a51-ad6d-4218217d9754/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/38aae825-f008-4323-8b5e-c51a2783e7cf/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/79c94712-8a80-4e7a-a3f4-a03353d57277/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/674937a9-8d08-400e-a692-a57a3b94ac18/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/7ac4c7c3-62dc-4b10-af28-ca6bf87f92a4/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/3411409c-17d5-4055-b55d-7f228a63c1e4/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/ad6a7a78-051e-403e-ac6f-1827d04e3e07/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/da0c6135-f99a-48bd-a870-f53206b99596/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/08fbfae7-886f-4066-b8bf-2d45641529cd/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/83d746a1-301d-4329-9ed7-5f3cc30d75b6/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/eec58eb3-1f1d-4b46-9839-94e45d3ab4ed/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/f8f99b9e-6c54-4c65-865f-5011df50428d/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/ffbeae4f-8daa-49f5-bd9c-2a9ab6443570/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/fb0182f8-a352-41de-8500-c7913fa3ac9d/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/75e4c393-516b-4435-a492-40d743a3b25a/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/a5250c91-6f37-40a7-b941-979575f979d2/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/768f72ae-8995-4f50-9d80-20c4ebf11564/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/13309ebe-68d5-4e0f-8412-974abe1d8e64/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/5f855e35-f98b-452d-a160-385102d99028/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/3b7c20b4-8393-4a25-bb94-80567330b99b/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/905604e9-da38-45bf-9e96-8a4cb78d2895/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/37461e6b-af0b-4ec3-a088-05aa0bdfb366/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/178008f6-9935-4598-860b-f3590ae6adb2/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/9866c8c3-fb01-4472-8197-19d85f12a71c/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/9aeab08d-a6ab-4ab4-b301-bf823a2bb505/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/abfe7032-d283-449f-875b-7f2d1bd24e8d/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/6c782193-0ccc-4517-8b05-44c6aee70389/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/5971e4f2-15c5-4ef2-8ed8-545405f0a9c6/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/302c5f4f-f21b-41eb-9b54-db2127014c05/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/138cfb18-79f4-4cd1-a2b1-fc1b0e8cf0e4/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/3210ceeb-f1cd-4fb8-bcef-c28880e231d3/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/fbee3dc2-fbcc-4f70-9538-f310fa21eb43/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/07eca365-0974-471d-bf2b-6103d36a7604/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/478c9a54-be20-4a4d-bb91-30260d02384c/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/b3c9579f-ee31-4400-a8fa-277a90a0042e/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/8724fbc7-000d-48dd-a98e-24d895d15cf5/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/3e4fa091-1be1-44f8-ba43-d71b5779afed/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/8582b055-0d7c-4471-a7ab-cf549735ec77/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/5f79aa81-80cd-4808-a275-32a40d00910c/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/b083f334-c746-4ffc-9aac-87c90c767238/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/9c5eef0e-0cc6-4a41-804f-1aab896c4d50/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/9f6d234d-b7a0-4f53-9ec8-a3a38f8c3e0f/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/fa12caf3-95de-44f7-9b9c-68493c54136c/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/bdedc641-bfd2-453a-9a0a-3dcd66ffc766/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/1b16278d-8a50-43e0-a2d2-78ec6d1d6f64/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/2e2c0fc7-f803-4f70-a1ec-8be46f60edbd/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/4b98ef5d-00e1-4c72-8de1-01d351d762ba/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/c91423b5-7cc9-4d0c-b042-10bdf82c20eb/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/23f252ee-f0bf-4996-90ce-11d222a86a43/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/734430d1-9132-4919-ba7e-d8884b445edc/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/18d3d1b8-d55c-48df-96d4-027eb36ea50c/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/59a8c267-ebaa-45fb-b950-a4b773bba17b/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/11cfd2bb-fed8-47d2-bab5-06f5684d1d78/out.png"
    }
  ],
  "started_at": "2022-12-06T21:22:42.147428Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/usdvqwoudja73m6vnlcgjy5mwq",
    "cancel": "https://api.replicate.com/v1/predictions/usdvqwoudja73m6vnlcgjy5mwq/cancel"
  },
  "version": "5793aa325d9e66f63b48a248291b7d552e03712bbf481224215d31aa906c609b"
}

Tweak it ShareReport

iteration: 0, render:loss: -0.67529296875
iteration: 10, render:loss: -1.03125
iteration: 20, render:loss: -1.1787109375
iteration: 30, render:loss: -1.365234375
iteration: 40, render:loss: -1.529296875
iteration: 50, render:loss: -1.5712890625
iteration: 60, render:loss: -1.619140625
iteration: 70, render:loss: -1.662109375
iteration: 80, render:loss: -1.650390625
iteration: 90, render:loss: -1.7021484375
iteration: 100, render:loss: -1.7392578125
iteration: 110, render:loss: -1.7568359375
iteration: 120, render:loss: -1.794921875
iteration: 130, render:loss: -1.7451171875
iteration: 140, render:loss: -1.771484375
iteration: 150, render:loss: -1.7890625
iteration: 160, render:loss: -1.8291015625
iteration: 170, render:loss: -1.82421875
iteration: 180, render:loss: -1.798828125
iteration: 190, render:loss: -1.7822265625
iteration: 200, render:loss: -1.8056640625
iteration: 210, render:loss: -1.8291015625
iteration: 220, render:loss: -1.8095703125
iteration: 230, render:loss: -1.873046875
iteration: 240, render:loss: -1.814453125
iteration: 250, render:loss: -1.8828125
iteration: 260, render:loss: -1.814453125
iteration: 270, render:loss: -1.798828125
iteration: 280, render:loss: -1.8583984375
iteration: 290, render:loss: -1.9130859375
iteration: 300, render:loss: -1.845703125
iteration: 310, render:loss: -1.8701171875
iteration: 320, render:loss: -1.9306640625
iteration: 330, render:loss: -1.9609375
iteration: 340, render:loss: -1.8740234375
iteration: 350, render:loss: -1.7841796875
iteration: 360, render:loss: -1.9423828125
iteration: 370, render:loss: -1.8515625
iteration: 380, render:loss: -2.001953125
iteration: 390, render:loss: -2.001953125
iteration: 400, render:loss: -1.9482421875
iteration: 410, render:loss: -1.984375
iteration: 420, render:loss: -1.94921875
iteration: 430, render:loss: -2.037109375
iteration: 440, render:loss: -1.986328125
iteration: 450, render:loss: -1.9921875
iteration: 460, render:loss: -2.015625
iteration: 470, render:loss: -2.05078125
iteration: 480, render:loss: -2.078125
iteration: 490, render:loss: -2.041015625
iteration: 500, render:loss: -2.048828125
iteration: 510, render:loss: -2.09765625
iteration: 520, render:loss: -2.078125
iteration: 530, render:loss: -2.048828125
iteration: 540, render:loss: -2.083984375
iteration: 550, render:loss: -2.033203125
iteration: 560, render:loss: -2.140625
iteration: 570, render:loss: -2.08203125
iteration: 580, render:loss: -2.0234375
iteration: 590, render:loss: -2.126953125
iteration: 599, render:loss: -2.109375

Prediction

kvfrans/clipdraw:1bbb18ae07d3ac65c2ae031ac759fd5354a84dcaf0fa7b838334bb048a2cafb8

Model

kvfrans/clipdraw:1bbb18ae

ypn6hovmkzbthcpt6f4f5jxrqe

Status

Succeeded

Source

Web

Hardware

–

Total duration

–

Created

over 3 years ago by @chenxwh

Input

prompt: a 3D rendering of a temple
num_paths: 256
num_iterations: 500
display_frequency: 20

{
  "prompt": "a 3D rendering of a temple",
  "num_paths": 256,
  "num_iterations": 500,
  "display_frequency": 20
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run kvfrans/clipdraw using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "kvfrans/clipdraw:1bbb18ae07d3ac65c2ae031ac759fd5354a84dcaf0fa7b838334bb048a2cafb8",
  {
    input: {
      prompt: "a 3D rendering of a temple",
      num_paths: 256,
      num_iterations: 500,
      display_frequency: 20
    }
  }
);

console.log(output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

Run kvfrans/clipdraw using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "kvfrans/clipdraw:1bbb18ae07d3ac65c2ae031ac759fd5354a84dcaf0fa7b838334bb048a2cafb8",
    input={
        "prompt": "a 3D rendering of a temple",
        "num_paths": 256,
        "num_iterations": 500,
        "display_frequency": 20
    }
)

# The kvfrans/clipdraw model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
    # https://replicate.com/kvfrans/clipdraw/api#output-schema
    print(item)

To learn more, take a look at the guide on getting started with Python.

Run kvfrans/clipdraw using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "kvfrans/clipdraw:1bbb18ae07d3ac65c2ae031ac759fd5354a84dcaf0fa7b838334bb048a2cafb8",
    "input": {
      "prompt": "a 3D rendering of a temple",
      "num_paths": 256,
      "num_iterations": 500,
      "display_frequency": 20
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2021-10-13T21:40:08.192057Z",
  "created_at": "2021-10-13T21:34:30.421702Z",
  "data_removed": false,
  "error": null,
  "id": "ypn6hovmkzbthcpt6f4f5jxrqe",
  "input": {
    "prompt": "a 3D rendering of a temple",
    "num_paths": 256,
    "num_iterations": 500,
    "display_frequency": 20
  },
  "logs": "/root/.pyenv/versions/3.8.12/lib/python3.8/site-packages/torchvision/transforms/functional_tensor.py:876: UserWarning: Argument fill/fillcolor is not supported for Tensor input. Fill value is zero\n  warnings.warn(\"Argument fill/fillcolor is not supported for Tensor input. Fill value is zero\")\niteration: 0, render:loss: -0.767578125\niteration: 20, render:loss: -1.2333984375\niteration: 40, render:loss: -1.380859375\niteration: 60, render:loss: -1.46875\niteration: 80, render:loss: -1.5146484375\niteration: 100, render:loss: -1.5537109375\niteration: 120, render:loss: -1.5546875\niteration: 140, render:loss: -1.541015625\niteration: 160, render:loss: -1.587890625\niteration: 180, render:loss: -1.5869140625\niteration: 200, render:loss: -1.640625\niteration: 220, render:loss: -1.5888671875\niteration: 240, render:loss: -1.63671875\niteration: 260, render:loss: -1.681640625\niteration: 280, render:loss: -1.646484375\niteration: 300, render:loss: -1.736328125\niteration: 320, render:loss: -1.6767578125\niteration: 340, render:loss: -1.708984375\niteration: 360, render:loss: -1.7412109375\niteration: 380, render:loss: -1.796875\niteration: 400, render:loss: -1.8154296875\niteration: 420, render:loss: -1.8125\niteration: 440, render:loss: -1.7978515625\niteration: 460, render:loss: -1.8515625\niteration: 480, render:loss: -1.80859375\niteration: 499, render:loss: -1.8671875",
  "metrics": {
    "total_time": 337.770355
  },
  "output": [
    {
      "file": "https://replicate.delivery/mgxm/fa8ce451-566d-4491-b4df-f9505ab714e7/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/463c4a3b-310b-4efb-bd0b-f7a3aa4562fe/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/3a865840-74a6-4a27-b251-a75afd736552/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/47e7c5db-e4e5-4fc3-85b9-596788a0a2bf/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/d57c035e-cb9e-43af-be15-828d1c5627ed/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/84176b96-ea36-4a73-aea9-5e5fc3cf55e2/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/f53a007b-a70f-4f10-9ea7-c5d62015397e/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/4499f1ed-238f-493c-9fd7-5409fbe40287/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/2ff1e676-1ebf-4c8a-81da-9cc86a81be83/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/8f20c69c-8a08-48b7-91cf-a8ba9f0b9054/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/0da177c0-9b54-4667-a145-3aca9f9549cc/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/54ba8903-758f-41e3-9a5c-9862972eec05/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/64c07c44-2e61-4651-91e9-2ef9e7f198ca/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/5a98b89d-113f-433c-9791-d21affaec94d/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/91d1ec5e-a1cb-4ed8-a505-ed3adc58cf78/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/e7e8dcab-ba8b-4f9a-9a15-de38ac0cc12c/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/3a3ff7b0-be6e-4efb-83a5-bcf0a4a890c7/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/5dffdab7-1023-4d61-8422-006ac1ffeb30/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/6bdf5057-fa1d-466b-8757-4328e62e5eaf/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/df3f74ab-e8a1-4e00-b2de-40349cf476be/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/a876114c-3a21-49b8-9931-9e42bc234ac5/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/dae4a353-89a9-4344-aac1-72afec76c88f/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/5ab4dfdb-f6a1-400b-93a0-bfef454f1058/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/701146c9-7f99-4b64-a519-bdb54d2334ab/out.png"
    },
    {
      "file": "https://replicate.delivery/mgxm/0986e041-7be2-4d63-9388-dd1cc6154961/out.png"
    }
  ],
  "started_at": "2021-12-01T09:05:06.207880Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/ypn6hovmkzbthcpt6f4f5jxrqe",
    "cancel": "https://api.replicate.com/v1/predictions/ypn6hovmkzbthcpt6f4f5jxrqe/cancel"
  },
  "version": "1bbb18ae07d3ac65c2ae031ac759fd5354a84dcaf0fa7b838334bb048a2cafb8"
}

Tweak it ShareReport

Want to make some of these yourself?

Run this model

kvfrans / clipdraw

Prediction

Input

Output

Prediction

Input

Output

Prediction

Input

Output

Logs (43runp6r7zax5mr2bntgbe6kia)

Logs (usdvqwoudja73m6vnlcgjy5mwq)

Logs (ypn6hovmkzbthcpt6f4f5jxrqe)